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Record W4409528331 · doi:10.1016/j.xkme.2025.101010

Variations in Creatinine Generation Among Patients With Glomerular Disease: Evidence From the NEPTUNE and CureGN Studies

2025· article· en· W4409528331 on OpenAlexfundno aff
Shalini S. Ramachandra, Melody Chiang, Michael Arbit, Dorey A. Glenn, Laura Mariani, Jarcy Zee

Bibliographic record

VenueKidney Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsnot available
FundersNephrotic Syndrome Study NetworkNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeNephcure FoundationNational Institute of Allergy and Infectious DiseasesHalpin FoundationNational Institutes of HealthRare Diseases Clinical Research NetworkBoehringer IngelheimNational Institute of Diabetes and Digestive and Kidney DiseasesChinook TherapeuticsTravere TherapeuticsSociety for the Study of ReproductionAlport Syndrome FoundationUniversity of Michigan
KeywordsCreatinineNeptuneRenal functionDiseaseMedicineUrologyInternal medicinePhysicsAstronomy

Abstract

fetched live from OpenAlex

Rationale & Objective Estimation of glomerular filtration rate (GFR) assumes that creatinine generation (crG) is relatively stable. This study identified factors associated with crG variability and its impact on serum creatinine changes ( Δ Scr) among patients with glomerular disease. Study Design An observational cohort study. Setting & Participants Nephrotic Syndrome Study Network and Cure Glomerulonephropathy adult and pediatric participants with at least one crG measurement. Predictors Potential predictors of crG levels included age, sex, disease diagnosis, weight status, estimated GFR (eGFR), urine protein, steroid use, and nonsteroid immunosuppressant use. crG change ( Δ crG) was then used as an exposure to assess impacts on Δ Scr. Outcomes crG levels and Δ Scr. Analytical Approach The intraclass correlation coefficient illustrated crG variability within individuals. Multivariable linear mixed models identified factors associated with crG levels. Among those with 2+crG measurements, multivariable linear mixed models estimated the association between Δ crG and Δ Scr. Results Among 4,626 crG measurements from 1,081 participants, there was only moderate correlation between measurements within individuals (intraclass correlation coefficient=0.517, 95% CI, 0.482-0.548) overall. For pediatric participants, factors significantly associated with crG included age, sex, weight status, and urine protein. Among adults, significant factors were age, sex, disease diagnosis, weight status, eGFR, steroid use, and nonsteroid immunosuppressant use. Limitations The 24-hour urine collections may have collection error, measured GFR was unavailable, and edema status was unavailable. Conclusions crG was highly dynamic within individuals over time and varied with glomerular disease activity and treatments. The impact of Δ crG on Δ Scr —and subsequently on estimation of kidney function—is potentially large. Accounting for these changes or development of alternative kidney function measures are needed among glomerular disease patients. Plain Language Summary Creatinine generation is often assumed to be stable when using creatinine to estimate kidney function and track kidney function over time, but it can vary with chronic disease. This study showed high variability in creatinine generation within individuals with glomerular disease. Besides age, sex, and weight, important factors that can impact creatinine generation include urine protein in children and kidney function, disease diagnosis, steroid use, and nonsteroid immunosuppressant use in adults. Changes in creatinine generation may also impact changes in the serum creatinine, which would influence estimates of kidney function, but this relationship needs to be studied further. In the meantime, accounting for factors affecting creatinine generation or using alternative estimates of kidney function in patients with glomerular diseases is needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.306
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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